[virtual] cells

introduction molecular biology biotechnology bioMEMS bioinformatics bio-modeling cells and e-cells transcription and regulation cell communication neural networks dna computing fractals and patterns the birds and the bees ….. and ants

course layout

introduction

size

size

size

Humans

60 trillion cells

320 cell types

cells

the cell theory

The Cell is the fundamental structural and functional unit of living organisms

The activity of an organism is dependent on both the individual and collective activities of its cells

Cell actions are determined and made possible by specific subcellular structures

Cells come from cells

the cellular basis of life

The cell is the unit of life: it contains everything needed to survive.

Complex carbon, hydrogen, oxygen, nitrogen and traces of others organized into multiple structures = organelles each type needed for survival

Many different shapes and sizes Neurons Blood cells ……

neuron

red blood cells

3 basic parts Nucleus Cytoplasm

all cellular contents between plasma membrane and nucleus organelles = specialized internal structures

Plasma membrane

cell components

cell components

cell components

lysosomes forms spindle fibres to separate chromosomes during cell division

golgi apparatus final packaging location for proteins and lipids and distribution

centriole modifies chemicals to make them functional; secretes chemicals in tiny vesicles; stores chemicals; may produce endoplasmic reticulum

endoplasmic reticulum transports chemicals between cells and within cells; - provides a large surface area for the organization of chemical reactions and synthesis

http://www.tvdsb.on.ca/westmin/science/sbi3a1/Cells/cells.htm

lipid layer

lipid layer

lipid layer

cytoplasm

Organelles to know Mitochondria Ribosomes Rough endoplasmic reticulum = Rough ER Smooth endoplasmic reticulum = Smooth ER Golgi apparatus Lysosomes Peroxisomes Nucleus Nucleoli

nucleus

cell’s control center usually visible nuclear envelope

double membrane nuclear pores in membrane allow passage of substances betwee

n cytoplasm and nucleus contains the hereditary material = DNA

carries instructions for making proteins determines cell structure, coordinates activities of the cell

nucleus

nucleolus

Nucleoli Darker staining, oval/spherical bodies within the nucleus Clusters of DNA, RNA, and protein (not membrane-bound) Site of ribosome assembly

cells

prokaryotic vs eukaryotic

Single cells but can be filamentous

Small streamlined genomes No complex organelles Fast cell cycle No membrane defined nucleus No large visible chromosomes

Membrane defined nucleus Complex cytoplasmic organelles Slow cell cycle Complex development Large genome with introns Multiple chromosomes

prokaryotic cell

prokaryotic cell

eukaryotic cell

Rough endoplasmic reticulum-site of secreted protein synthesis Smooth ER-site of fatty acid synthesis Ribosomes-site of protein synthesis Golgi apparatus- site of modification and sorting of secreted proteins Lysosomes-recycling of polymers and organelles Nucleus-double membrane structure confining the chromosomes Nucleolus-site of ribosomal RNA synthesis and assembly of ribosomes Peroxisome-site of fatty acid and amino acid degradation Flagella/Cilia- involved in motility Mitochondria-site of oxidative phosphorylation Chloroplast-site of photosynthesis Intermediate filaments- involved in cytoskeleton structure

eukaryotic cell

eukaryotic cell

eukaryotic cell

plant vs animal cells

Plant cells have chloroplasts and perform photosynthesis

Outermost barrier in plant cells is the cell wall

Outermost barrier in animal cells is the plasma membrane

plant cell

5 µm

plant cell

1 µm

plant cell

chloroplast

20 µm

chloroplast

mitochondria

break large molecules into small molecules by inserting a molecule of water into the chemical bond

produces energy

mitochondria

mitochondria

cell evolution

cell evolution

cell evolution

T4 bacteriophage

tobacco mosaic virus

50 nm

adenovirus

50 nm

transport

plasma membrane: structure

plasma membrane

Membrane Chemistry and Anatomy 50-50 split in weight ratio: lipid/protein More lipid molecules than protein molecules because of proteins’

larger sizes Membrane lipids

Phospholipids 75% Glycolipids 5% Cholesterol 20%

structure

plasma membrane

Determine the functions a cell can perform Composition varies widely among cell types

Integral proteins – located within the membrane channels transporters receptors intracellular junctions enzymes cytoskeleton anchors cell identity markers peripheral proteins - located on either face of the membrane A similar list of many functions

proteins

fluid mosaic model

Dynamic “fluid” structure Constantly changing components

molecular positions less often its composition individual molecules recycled

The kinds and numbers of membrane molecules, especially proteins, determine membrane function.

structure

Communication with other cells and tissues selective permeability - allows passage of some

substances, limits others dependent on:

molecular size lipid solubility charge

membranes impermeable to all charged molecules

Ions only move through membrane through channels

The presence of channels & transporters is very specific

functions

fluid mosaic model

passive transport

Moves materials across cell and organelle membranes without expending cellular energy

Simple Diffusion kinetic energy is everywhere - allows mixing or diffusion diffusion requires a concentration gradient

high concentration in one area, lower concentration in another

if areas continuous (connected) particles move with (down) the concentration gradient

eventually reaches equal concentration everywhere - equilibrium

passive transport

Diffusion through the plasma membrane selective permeability water and lipid-soluble molecules move freely through membra

ne small non-lipid-soluble substances may move through specific

channels

simple diffusion

simple diffusion

factors affecting diffusion

increased temperature increases diffusion rate greater concentration gradients increase diffusion rate larger surface area increases diffusion rate smaller particle sizes increase diffusion rate time - diffusion decreases as concentrations equalize

Osmosis is the movement of water from an area of higher [H2

O] to an area of lower [H2O] concentration water moves with (down) its concentration gradient

osmotic pressure – the net pressure effect of individual particles in solution

hydrostatic pressure = fluid pressure is created by osmosis

osmosis

osmosis

solution tonicity

isotonic solution cells in a solution where [ ] of solutes is same inside/outside cell

s no net movement of water, e.g., “normal” saline solution

hypertonic solution cells in a solution with an increased [ ] of solutes water moves out of cells, cells shrink, crenation

hypotonic solution cells in a solution with a decreased [ ] of solutes water moves into cells, cells swell, may rupture

facilitated diffusion

some substances too large to diffuse need help crossing integral proteins move substances into cells – with (down) the

concentration gradient passive - no cellular energy required may be regulated by hormones

example insulin will increase cellular glucose uptake

facilitated diffusion

active transport

some substances cannot move passively they may be too big they may have the wrong charge they must be moved against concentration gradient

energy must be expended for active processes (they require the energy derived from splitting ATP = energy of hydrolysis)

uses ATP hydrolysis to power transport many substances move by primary active transport: Na+, K+, H

+, Ca++, I-, Cl-, amino acids, monosaccharides, etc. two substances may be coupled for secondary active transport

= co-transport = facilitated transport Symport/Symporter – transports substances in the same directio

n Antiport/Antiporter – transports substances in the opposite directi

on

active transport

primary active transport

uses ATP hydrolysis energy directly to move substances ATP is hydrolyzed to move specific

ions without ATP, the pump does not wo

rk

Na+/ K+ ATPase pumps 3 Na+ out / 2 K+ in each cy

cle because Na+ & K+ always leaks acr

oss the membrane, the pump is always working

secondary active transport

uses ATP energy indirectly to move substances uses the concentration gradient from a primary active transport

er for transport (like a water mill grinding corn) as Na+ leaks back into cell, bound to the symport, the symport

binds and drags the glucose inside with Na+

secondary active transport

resting membrane potential

Generating/maintaining a resting membrane potential all cells are polarized

negatively charged inside positively charged outside

Na+/K+ ATPase creates the unequal charge distribution Na+ tends to diffuse in on its own K+ tends to diffuse out on its own the sodium-potassium pump transports 3 Na+ out & 2

K+ in each cycle this creates the charge differential

Electrochemical gradient the net effect of all charged ions on either side of the

membrane

resting membrane potential

cell-environment interactions

Membrane Receptors contact signaling - identifying neigh

bor cells electrical signaling - channels resp

onding to voltage changes (concentrations of charged ions)

chemical signaling – various signal compounds: neurotransmitters, hormones, and local hormones

cell division

cell cycle

budding S. cerevisiae

cell cycle

control of the cell cycle

19 to 24 hrs in mammalian cells DNA replication Cyclins Kinases Phosphorylation

mitosis

Cell cycle DNA replication Somatic cells 2n to 2n No pairing

Somatic cells DNA replication 2n to 2n

mitosis

mitosis

mitosis

mitosis

human chromosomes

meiosis

DNA replication Homologous pairing Recombination Reduction division Germline cells

meiosis

Germ line cells DNA replication Homologous pairing Recombination Reduction-division

2n to n

meiosis

meiosis

meiosis

meiosis

meiosis

meiosis

10 µm

labeling antibodies

cell signaling

cell signaling

cell differentiation

cell differentiation

Mendelian genetics

definitions

Gene- a unit of heredity Allele- a form of a gene Dominance- one allele dominants or masks the other Recessive- only seen/expressed in the homozygous

state Homozygous- having two of the same allele Heterozygous- having two different alleles

Aa x Aa

A aA

aAA AaAa aa

Punetts square

monohybrid cross

monohybrid cross

monohybrid cross

dihybrid cross

Mendel’s postulates

Heredity units in pairs Dominance/recessive Segregation of unit factors Independent assortment of factors

virtual cells

cell simulation

virtual cells

virtual cell

http://www.nrcam.uchc.edu/

applications

Energy-Metabolism in E. coli E-rice Human Erythrocyte model Circadian rhythms E-neuron Chemotaxis

the cell as a simulation target

self sustaining cell

motivation

Genome sequencing and functional analysis of complete gene sets are producing a huge mass of molecular information for a wide range of model organisms. Previous work in genetic simulation has isolated well-characterized pathways for detailed analysis, but methods for building integrative models of the cell that incorporate gene regulation, metabolism and signaling have not been established until a few years ago.

By attempting to understand the dynamics in living cells, we should be able to predict consequences of changes introduced into the cell.

Possible consequences of such intervention include changes in cell death, growth rate, and an increase or decrease in the expression of specific genes.

The development of sufficiently refined cell models which allow predictions of such behavior would complement the experimental efforts now being made systematically to modify and engineer entire genomes.

motivation

So, there was need in a software environment for building integrative models based on gene sets, and running simulations to conduct experiments in silico.

Different approaches are used:1. Ordinary differential equations (ODE),2. π-calculus formal language,3. Hybrid functional Petri nets (HFPN),etc.

motivation

E-CELL

E-CELL is a modeling and simulation environment for simulation with GUI, based on ODE.

Biochemical reactions are represented as a systems of ODEs.

For reactions which cannot be represented with ODEs, it employs ad-hoc user defined C++ programs.

implementation of the E-CELL

The E-CELL is a rule-based simulation system, written in C++. The model consists of three lists, and is loaded at runtime.

1.The substance list defines all objects which make up the cell and the culture medium. 2. The rule list defines all of the reactions which can take place within the cell.3. The system list defines functional structure of the cell and its environment.

The state of the cell is expressed as a list of concentration values of all substances, pH and temperature.

In a single time interval, each rule in the rule list is called upon by the simulator engine to compute the change in concentration of each substance.

E-CELL allows the assignment of any numerical integration algorithm for each compartment of the cell model, as well as definition of different time intervals (Δt).

The system defaults to 1 ms for Δt and the user can select between the first-order Euler or fourth-order Runge-Kutta methods for the numerical integration in each compartment.

implementation of the E-CELL

user interfaces of the E-CELL

The E-CELL provides the following graphical interfaces:1. The tracer interface allows to select substances or reactions and observe dynamic changes.2. The substance window shows the exact quantity of a selected substance.3. The reactor window displays the activity of a selected reaction. 4. The gene map window provides the user with a means of monitoring the expression level of all genes. It also allows the user to knock out a selected gene or group of genes.

user interfaces of the E-CELL

modeling the cell

The main purpose is to develop a framework for constructing simulatable cell models based on gene sets derived from completed genomes.1. A model of a hypothetical, minimal cell, based on the gene set of Mycoplasma genitalium, the self-replicating organism having the smallest known genome was constructed. Its gene set was reduced to only those genes that are required for what was defined as a minimal cellular metabolism.

modeling the cell

This model cell takes up glucose from the culture medium using a phosphotransferase system, generates ATP by catabolizing glucose to lactate through glycolysis and fermentation, and exports lactate out of the cell.

The model cell is 'self-supporting', but not capable of proliferating; the cell does not have pathways for DNA replication or the cell cycle.

The cell model is basically constructed with three classes of objects: Substances, Genes and reaction rules. The reactions rules are internally represented as Reactor objects.

modeling the cell

structure of the E-CELL system

substances

All molecular species within the cell are defined as Substances. The same molecule in different states (e.g. phosphorylation) is defined as separate molecular species and each spatial compartment of the model retains a list of all of the substance objects it may contain.

genes

DNA sequences in chromosomes are modeled as a doubly linked list of Genomic Elements.

The genome of the cell consists of 127 genes including 20 tRNA genes and two rRNA genes. 1. Out of the 127 genes, 120 have been identified in the genome of M.genitalium.2. The last of the seven E-CELL genes not found in M.genitalium is glutamine-tRNA ligase, whose function is probably substituted for by glutamate-tRNA ligase in M.genitalium

tables

The number of genes in important pathways of the hypothetical cell.

Protein coding genes in the hypothetical cell.

Enzymes in the hypothetical cell.

Small molecules in the hypothetical cell.

http://web.sfc.keio.ac.jp/~mt/mt-lab/publications/Paper/ecell/graphics/btc007t03.gif

reaction rules

A typical reaction in a metabolic pathway is transformation of one molecular species into another, catalyzed by an enzyme which remains unaltered. For example, the enzyme 6-phosphofructasokinase (EC 2.7.1.11) catalyzes the transformation of d-fructose 6-phosphate (C00085) into d-fructose 1,6-biphosphate (C00354), consuming ATP (C00002) and generating ADP (C00008) and H+ (C00080) (E-CELL Substance IDs shown in parentheses):

C00085 + C00002 -> C00354 + C00008 + C00080 [EC 2.7.1.11]

Pathways can then be implemented by defining a series of reactions which use the products of another reaction as participating reactants.

transcription and translation

Complex reactions such as transcription and translation are modeled in detail as a series of reactions.

The system does not have any regulatory factors, such as repressors and enhancers, although they may be added by the user.

transcription and translation

A generalized chemical reaction:

where Sn is a concentration of the nth substance and [nu]n is a stoichiometric coefficient for the substance.

Most non-enzymatic reactions are first-order reactions. Their velocities directly depend on concentrations of the substrates and can be expressed as:

reaction kinetics

Enzymatic reaction with a substrate and a product can be expressed as the Michaelis-Menten equation:

where Vmax is the maximal velocity of the reaction and Km is the Michaelis constant.

For multiple substrates/products computation is a bit more complicated.

reaction kinetics

virtual experiments

The E-CELL interfaces provide a means of conducting experiments in silico. For example, we can 'starve' the cell by draining glucose from the culture medium. The cell would eventually 'die', running out of ATP. If glucose is added back, it may or may not recover, depending on the duration of starvation. We can also 'kill' the cell by knocking out an essential gene for, for example, protein synthesis. The cell would become unable to synthesize proteins, and all enzymes would eventually disappear due to spontaneous degradation.

a trace of the quantity of ATP in the starving cell

virtual experiments

a trace of mRNA levels before and after starvation of the cell

virtual experiments

genome engineering

The main purpose of the E-CELL is to model the real cell of M.genitalium, the organism having the smallest known chromosome. Because of the small number of genes (470 proteins, 37 RNAs), M.genitalium is a prime candidate for exhaustive functional (proteome) analysis. Because there are still many genes whose functions are not yet known, it will probably be necessary to hypothesize putative proteins to complement missing metabolic functions, in order for the model cell to work in silico.

genome engineering

E-CELL is applicable for:

Finding the optimal nutritional environment. Deciphering gene regulatory networks. Defining the minimal set of genes required for a self-replicating

.

Further task: to allow the cell proliferate. Further investigation goals:

1. Comparison of living cells to their computer models in order to refine the system.2. Defining minimal gene set in order to create such living cells for future experiments.

concluding remarks on E-CELL

BioPSI

BioPSI is a computer system, developed for the representation and simulation of biochemical processes

BioPSI is based on stochastic π-calculus. π-calculus is a formal language originally developed for specifyi

ng concurrent computational systems.

MCell

A General Monte Carlo Simulator of Cellular Microphysiology

… MCell now makes it possible to incorporate high resolution ultrastructure into models of ligand diffusion and signaling …

what is MCell ?

MCell uses Monte Carlo diffusion Chemical reaction algorithms in 3D

MCell simulates Release of ligands in solution Creation/destruction of ligands Ligand diffusion within spaces Chemical reactions undergone by ligand and

effector

what is MCell ?

what is MCell ?

what is MCell ?

main biochemical interactions

3D diffusion of ligand molecules based on Brownian motion

the average net flux from one region of space to another depends on molecules mobility depends on 3D concentration gradient between the regions

computing 3D gradients

With VoxelsAssume well-mixed condition Use PDEs for average net changes

PROS correct average system behavior

CONS too complex for realistic structures output has no direct stochastic information

computing 3D gradients

Monte Carlo approach Directly approximate the Brownian movements of the

individual ligand Chemical reaction rates are solution rate const

PROS events are considered on a molecule-by-molecule

basis the simulation results include realistic stochastic noise

CONS complexity

how to run MCell ?

Simulate the system behavior Running the same computation with different seeds Averaging all the instances

Each instance has A pre-defined number of time steps Input data

how to run MCell ?

Input Data consists of one or more MDL scripts files describing elements of the simulation

spatial geometryeffector locationchemicals' repartitions

Output files resulting stochastic model visualization files